Lands the matrix indexer's first piece per docs/SPEC.md §3.4:
multi-corpus retrieve+merge with corpus attribution per result.
Future components (relevance filter, downgrade gate, learning-loop
integration) layer on top of this surface.
Architecture:
- internal/matrix/retrieve.go — Retriever takes (query, corpora,
k, per_corpus_k), parallel-fans across vectord indexes, merges
by distance ascending, preserves corpus origin per hit
- cmd/matrixd — HTTP service on :3217, fronts /v1/matrix/*
- gateway proxy + [matrixd] config + lakehouse.toml entry
- Either query_text (matrix calls embedd) or query_vector
(caller pre-embedded) — vector takes precedence if both set
Error policy: fail-loud on any corpus error. Silent partial returns
would lie about coverage, defeating the matrix's whole purpose.
Bubbles vectord errors as 502 (upstream), validation as 400.
Smoke (scripts/matrix_smoke.sh, 6 assertions PASS first try):
- /matrix/corpora lists indexes
- Multi-corpus search returns hits from BOTH corpora
- Top hit is the globally-closest across all corpora
(b-near beats a-near at distance 0.05 vs 0.1 — proves merge)
- Metadata round-trips through the merge
- Distances ascending in result list
- Negative paths: empty corpora → 400, missing corpus → 502,
no query → 400
12-smoke regression sweep all green (D1-D6, G1, G1P, G2,
storaged_cap, pathway, matrix).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
254 lines
8.0 KiB
Go
254 lines
8.0 KiB
Go
// Package matrix is the multi-corpus retrieval layer above vectord.
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// Per docs/SPEC.md §3.4: the matrix indexer composes N single-corpus
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// vectord indexes into one retrieve+merge surface, with corpus
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// attribution preserved per result. Future work in the same package:
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// relevance filter, strong-model downgrade gate, learning-loop
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// integration. This file is component 2 of the dependency-ordered
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// port plan — multi-corpus retrieve+merge, no filter yet.
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//
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// Why corpus-as-shard rather than hash-shard a single index:
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// different corpora have distinct topology and distinct retrieval
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// intent (workers vs candidates vs scrum_findings vs lakehouse_arch).
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// Multi-corpus search merges across them by distance — that IS the
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// matrix indexer's whole purpose. See feedback_meta_index_vision.md
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// and project_small_model_pipeline_vision.md.
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package matrix
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import (
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"bytes"
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"io"
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"net/http"
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"sort"
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"sync"
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"time"
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"git.agentview.dev/profit/golangLAKEHOUSE/internal/vectord"
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)
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// Result is one merged hit with corpus attribution. The corpus field
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// is load-bearing — losing it would defeat the matrix's purpose
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// (knowing WHICH corpus contributed each hit is half the signal).
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type Result struct {
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ID string `json:"id"`
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Distance float32 `json:"distance"`
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Corpus string `json:"corpus"`
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Metadata json.RawMessage `json:"metadata,omitempty"`
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}
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// SearchRequest is the matrix search input. Either QueryText (matrix
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// embeds it via embedd) or QueryVector (already embedded by caller)
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// must be set; QueryVector takes precedence if both supplied.
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type SearchRequest struct {
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QueryText string `json:"query_text,omitempty"`
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QueryVector []float32 `json:"query_vector,omitempty"`
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Corpora []string `json:"corpora"`
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K int `json:"k"`
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PerCorpusK int `json:"per_corpus_k,omitempty"`
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Model string `json:"model,omitempty"`
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}
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// SearchResponse wraps the merged results plus per-corpus return
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// counts so callers can detect "this corpus returned nothing"
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// without re-querying.
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type SearchResponse struct {
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Results []Result `json:"results"`
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PerCorpusCounts map[string]int `json:"per_corpus_counts"`
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}
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// Retriever holds the HTTP clients to embedd and vectord. Stateless
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// otherwise — safe to share across goroutines.
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type Retriever struct {
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httpClient *http.Client
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embeddURL string
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vectordURL string
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}
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// New returns a Retriever configured to call embedd at embeddURL
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// and vectord at vectordURL (both gateway-internal upstreams,
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// usually 127.0.0.1:3216 and :3215 respectively).
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func New(embeddURL, vectordURL string) *Retriever {
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return &Retriever{
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httpClient: &http.Client{Timeout: 30 * time.Second},
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embeddURL: embeddURL,
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vectordURL: vectordURL,
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}
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}
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// Errors surfaced to HTTP handlers.
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var (
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ErrEmptyCorpora = errors.New("matrix: corpora must be non-empty")
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ErrEmptyQuery = errors.New("matrix: query_text or query_vector required")
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ErrCorpus = errors.New("matrix: corpus search failed") // wraps vectord errors
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ErrEmbed = errors.New("matrix: embed failed")
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)
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// Search runs the matrix retrieve+merge.
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//
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// Error policy: fail-loud on any corpus error. Silent partial results
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// would lie about what was actually searched, which defeats the
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// indexer's coverage guarantee. Callers that want best-effort can
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// catch the error and re-issue with a smaller corpora list.
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func (r *Retriever) Search(ctx context.Context, req SearchRequest) (*SearchResponse, error) {
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if len(req.Corpora) == 0 {
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return nil, ErrEmptyCorpora
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}
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if req.K <= 0 {
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return nil, errors.New("matrix: k must be > 0")
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}
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if req.PerCorpusK <= 0 {
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req.PerCorpusK = req.K
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}
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// Resolve query → vector.
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qvec := req.QueryVector
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if len(qvec) == 0 {
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if req.QueryText == "" {
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return nil, ErrEmptyQuery
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}
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v, err := r.embed(ctx, req.QueryText, req.Model)
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if err != nil {
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return nil, fmt.Errorf("%w: %v", ErrEmbed, err)
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}
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qvec = v
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}
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// Parallel search across corpora. Each shard is independent;
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// fan-out + collect with WaitGroup is cleaner than channels-only.
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type shardResult struct {
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corpus string
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hits []vectord.Result
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err error
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}
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results := make([]shardResult, len(req.Corpora))
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var wg sync.WaitGroup
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for i, c := range req.Corpora {
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wg.Add(1)
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go func(i int, corpus string) {
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defer wg.Done()
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hits, err := r.searchCorpus(ctx, corpus, qvec, req.PerCorpusK)
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results[i] = shardResult{corpus: corpus, hits: hits, err: err}
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}(i, c)
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}
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wg.Wait()
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var allHits []Result
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perCorpus := make(map[string]int, len(req.Corpora))
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for _, s := range results {
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if s.err != nil {
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return nil, fmt.Errorf("%w: %s: %v", ErrCorpus, s.corpus, s.err)
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}
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perCorpus[s.corpus] = len(s.hits)
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for _, h := range s.hits {
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allHits = append(allHits, Result{
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ID: h.ID, Distance: h.Distance, Corpus: s.corpus, Metadata: h.Metadata,
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})
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}
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}
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// Stable sort so equal-distance ties keep input order (which is
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// per-corpus order from vectord's HNSW result heap). This matters
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// for deterministic test assertions.
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sort.SliceStable(allHits, func(i, j int) bool {
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return allHits[i].Distance < allHits[j].Distance
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})
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if len(allHits) > req.K {
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allHits = allHits[:req.K]
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}
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return &SearchResponse{Results: allHits, PerCorpusCounts: perCorpus}, nil
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}
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// Corpora returns the list of vectord index names. Thin proxy to
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// GET /vectors/index — exposed at the matrix layer so callers don't
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// need direct vectord access.
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func (r *Retriever) Corpora(ctx context.Context) ([]string, error) {
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url := r.vectordURL + "/vectors/index"
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httpReq, err := http.NewRequestWithContext(ctx, http.MethodGet, url, nil)
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if err != nil {
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return nil, err
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}
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resp, err := r.httpClient.Do(httpReq)
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if err != nil {
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return nil, err
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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b, _ := io.ReadAll(resp.Body)
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return nil, fmt.Errorf("vectord index list: status %d: %s", resp.StatusCode, b)
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}
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var out struct {
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Names []string `json:"names"`
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}
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if err := json.NewDecoder(resp.Body).Decode(&out); err != nil {
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return nil, err
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}
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return out.Names, nil
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}
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// embed POSTs a single-text /embed call. Reuses embedd's batched
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// /embed shape with len(texts)==1; embedd's LRU cache absorbs
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// repeat queries (commit 56844c3).
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func (r *Retriever) embed(ctx context.Context, text, model string) ([]float32, error) {
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body, err := json.Marshal(map[string]any{"texts": []string{text}, "model": model})
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if err != nil {
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return nil, err
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}
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httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, r.embeddURL+"/embed", bytes.NewReader(body))
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if err != nil {
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return nil, err
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}
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httpReq.Header.Set("Content-Type", "application/json")
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resp, err := r.httpClient.Do(httpReq)
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if err != nil {
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return nil, err
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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b, _ := io.ReadAll(resp.Body)
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return nil, fmt.Errorf("embed status %d: %s", resp.StatusCode, b)
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}
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var out struct {
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Vectors [][]float32 `json:"vectors"`
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}
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if err := json.NewDecoder(resp.Body).Decode(&out); err != nil {
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return nil, err
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}
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if len(out.Vectors) == 0 {
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return nil, errors.New("embed returned no vectors")
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}
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return out.Vectors[0], nil
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}
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// searchCorpus calls vectord /vectors/index/{name}/search.
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func (r *Retriever) searchCorpus(ctx context.Context, corpus string, vec []float32, k int) ([]vectord.Result, error) {
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body, err := json.Marshal(map[string]any{"vector": vec, "k": k})
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if err != nil {
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return nil, err
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}
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url := r.vectordURL + "/vectors/index/" + corpus + "/search"
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httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(body))
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if err != nil {
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return nil, err
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}
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httpReq.Header.Set("Content-Type", "application/json")
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resp, err := r.httpClient.Do(httpReq)
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if err != nil {
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return nil, err
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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b, _ := io.ReadAll(resp.Body)
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return nil, fmt.Errorf("status %d: %s", resp.StatusCode, b)
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}
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var out struct {
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Results []vectord.Result `json:"results"`
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}
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if err := json.NewDecoder(resp.Body).Decode(&out); err != nil {
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return nil, err
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}
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return out.Results, nil
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}
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